The growing awareness of depleting fossil fuels and climate change has motivated the electricity supply industry to constantly explore sustainable and scalable alternatives. Considering the innovation in generation technology alone, globally, the focus is on the integration of Renewable Energy Sources (RESs), like solar and wind-based power generation into the electricity grid. At the same time, deregulation has opened up various options to consumers to optimise their own energy usage. However, both of these trends have started to transform the paradigm of power system operation. Dealing with the highly volatile nature of RESs and unpredictable load behaviour, has become a significant issue for grid operators. Today, the supply intermittency and uncertainty of RESs and load is associated with a higher forecast error. Moreover, this supply uncertainty varies at different time scales, with higher levels at the time scale of generation planning, (i.e. a day), and at reduced levels on the time scale of control, (i.e., in seconds). Nevertheless, even in Real-Time (RT), the forecast error coupled with sudden supply fluctuations can be large enough to impact the electricity demand and supply balance significantly, which leads to poor frequency stability and increased operational cost. In view of these issues frequency regulation services need to be more flexible and capable of fast action to ensure system stability, and holistically designed to ensure cost optimality. Apart from the uncertainty in demand and supply, the additional important factor to the sub-optimal operational cost is a hierarchical approach of decision making such as centralised Economic Dispatch (ED) for generation allocation followed by local Automatic Generation Control (AGC) for frequency regulation.

In cognisance of these issues, the main objectives of this PhD project are to develop new intelligent distributed control strategies for frequency regulation. These methodologies are developed to improve the frequency response under volatile generation and load conditions by using faster resources for regulation services, and to achieve optimal electricity dispatch under system constraints in RT. These optimal control methods can potentially also defer additional infrastructure investments and maximise the utilisation of RESs and the interconnection network. The research is categorised into four sections.

As a first step to algorithm development, an Embedded Integrator based Distributed Model Predictive Control (EIDMPC) scheme is developed, which utilises a fast acting Demand Response (DR) alongside Governor Response (GR) for frequency control. A system model for each control area is first developed with DR and GR as manipulated input variables and the Area Control Error (ACE) as an output control variable. Then an EIDMPC scheme is formulated to obtain an optimal linear feedback control law to achieve a high computation speed, and the closed loop stability is assessed. The dependence of EIDMPC on the communication network is discussed and a model to handle communication loss is also given. The simulation studies are conducted on a two area interconnected power system in MATLAB, and results are discussed, showing benefits of the EIDMPC scheme.

The second algorithm development addresses cost optimisation. Here, a centralised optimisation problem is formulated for an interconnected power system, which combines the objectives of Economic Dispatch (ED) and Automatic Generation Control (AGC) in view of the network flow thermal limits. A distributed AGC law is derived from the formulation using a log-barrier approximation approach, which converges to a steady-state solution with minimal distance from that of the centralised formulation. This AGC law namely, Network Constrained Optimal AGC (NCOAGC), regulates frequency deviations in a cost-optimal manner while restricting power flow in the tie-lines to be within their thermal limits. The stability of the NCOAGC law is proven and numerical studies are conducted to substantiate the performance benefits.

Next, the research is extended to overcome the assumptions used in the development of NCOAGC algorithm by considering practical aspects of an interconnected power system, such as multiple generators and different generation technologies within a single control area. The enhancements for NCOAGC are identified and an algorithm is proposed to find the controller gains for NCOAGC under such scenarios. The algorithm is then tested with dynamic bus voltages and nonlinear network flows by developing an interconnected power system model with 4 control areas and 40 buses in the DIgSILENT PF 2017 simulation platform.

Finally, a new dynamic control formulation is proposed to accommodate the generation constraints in addition to the network flow thermal limits to the optimisation problem by using a State Constraint Distributed Model Predictive Control (SCDMPC) scheme. The SCDMPC also improves the dynamic optimal performance. A prediction model to handle communication delay is developed and a functional observer to estimate the unmeasured feed forward disturbance is also developed. Since the SCDMPC optimisation problem has the input as well as state constraints, an algorithm is proposed to handle infeasible scenarios. The infeasibility handling algorithm does not increase the number of unknowns and thus the computation time is not impacted.

At the end, the thesis is concluded and scope for future research is identified.